Abstract
Quantitative evidence from a number of historical and more recent sources indicate that the amount of science news in the mass media is not a constant; science news comes in irregular waves. The chapter constructs a continuous time-series index of expansion and of contraction between 1820 and 2010, locates the ‘high’ and ‘low’ years of news coverage, presents these as an indicator of fluctuating public attention to science that is corroborated by period studies. Six competing hypotheses are examined to explain the coming and going of public attention, of which the paradox of public communication of science, i.e. the re-enchantment of a disenchanted world, is the most promising one. The ‘longue duree’ picture, recovering nearly 200 years, shows that the recent expansion of science news making since the 1980s is historically unprecedented, and the chronology of ups and downs in public attention could constitute the spine of a historical narrative of the public understanding of science that still needs to be weaved into text.
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Notes
- 1.
I worked on the problem of long-term trends in media coverage of science in the early 1990s, but with little resonance, except maybe among Chinese colleagues who keenly discussed my presentation in Beijing in 1995, and at a colloquium at Paris VII in 1996, where the late Dorothy Nelkin very much encouraged this seemingly irrelevant post-doctoral concern. Subsequently, the historical literature and the data assembled at the time moved into an office box and collected dust. I was rather delighted when the Bielefeld group invited me to revisit this idea for the present project, maybe an indication that its time has come.
- 2.
We conducted our research in the early 1990s, when on-line resources were not yet at hand. Systematic data collection was a rather laborious undertaking involving many visits to the British Newspaper Library making photocopies from microfiche. Today’s bottleneck is no longer data collection, but data analysis.
- 3.
In the nineteenth century Nature was a journal specializing in science, while the Athenaeum was a general read of the educated classes with an interest in science among other things; the proliferation of specialist science journals which nowadays makes Nature a very general basic research journal only happened in the early twentieth century.
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The name ‘Kondratjeff cycle’ honours its ‘inventor’, the Russian economist Nikolai Dimitriyevich Kondratjeff (1892–1928), an agricultural economist who founded in 1920 the Russian Conjuncture Institute and died in a Siberian labour camp. He developed the hypothesis that the world economy would expand and contract in cycles of about 50 years, and this was endogenous to the capitalist economy; what brought him into conflict with his masters was the possibility that capitalist economies might recover with as much regularity as they get into crisis. Later economists such as Schumpeter and others developed this hypothesis and linked it to inventions and innovations. Inventions are swarming in depression periods, and each new upswing is characterised by a new scientific technological impulse, hence the naming of the cycles as ‘cotton-iron-steam’ or ‘railroadization’ or ‘electricity and automobile’, etc. (see Van Duijn 1983). Many historians such as Maddison are critical of the very existence of these ‘cycles’ and see more exogenous shocks such as wars and policy ideas as the source of long-term changes in growth rates.
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Appendix: How to Calculate the Index of Public Attention?
Appendix: How to Calculate the Index of Public Attention?
The data for the continuous ‘index of science news fluctuations between 1820 and 2006’ as shown in Fig. 3.2 (above) is constructed by way of the following steps involving the transformation and standardisation of data from different sources. This allows us to chain up several data series for different periods and reach a continuous estimate of long-term fluctuations around an unknown, but most likely upward trend in overall volume.
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The individual data series are transformed into standardised z-values, irrespective of what is being counted: \(z = \left(X - M_{{\textrm{series}}}\right){/}{\textrm{SD}}_{{\textrm{series}}}\). With this procedure any trend is taken out of the data. The missing years are interpolated. The result is a bi-annual data series for each study for which the variability becomes comparable on the same scale with an average, M = 0, and standard variation, SD = 1. One SD contains 68 percent of the variability assuming a normal distribution. The result of this transformation is presented in Fig. 3.1.
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In an Excel file, these data series are then ordered on a continuous time-line from 1820 to 2010. However, z-values do not guarantee that the end of one series neatly matches with the beginning of the next series; therefore, step three:
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Several of the data series overlap. This allows to average the overlapping years and to chain the discontinuous data series on these estimates, e.g., LaFollette’s and my own studies overlap for 10 years between 1946 and 1955; for these years, I average the z-values of step 1 and create a combined series from 1905 to 1992. For some periods where there are several studies, I average z-values for that period with \(z_t = \left(z_1 + {\textrm{z}}_2 + z_k \right){/k}\) and \({\textrm{SD}}_z \left(z_1 ,\;z_k \right)\). k is the number of overlapping studies in each year. Working through all the data series results in what is shown as bars in Fig. 3.2: bi-annual estimates of deviations from an overall, most likely exponential trend of volume.
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Because each bi-annual z-value remains an uncertain estimate, moving averages give a more reliable picture by smoothing over short-term fluctuations. Thus in addition to bi-annual averages (bars), I report 10-year moving average, the continuous thick line in Fig. 3.2, and a 25-year moving average, the dashed thick line in Fig. 3.2. A moving average is a mean of means for a fixed number of years: e.g., \(Z_t = \left(z_{t - 5} + \ldots + z_{t - 1} \ldots + z_{t + 1} + \ldots + z_{t + 5} \right){/10}\).
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To document the uncertainties in the overall picture, I plot the moving average of the standard deviations (SD) that arise from combining the overlapping data series in step 3. Figure 3.2 shows the band of confidence for 10-year averages, considering one moving standard deviation on either side: \({\textrm{BC}} = Z_{10{\textrm{year}}} + / - 1\break {\textrm{SD}}_{{\textrm{10year}}}\). Based on my database, with 68 percent certainty the ‘true’ value of public attention should fall within that band.
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Bauer, M.W. (2012). Public Attention to Science 1820–2010 – A ‘Longue Durée’ Picture. In: Rödder, S., Franzen, M., Weingart, P. (eds) The Sciences’ Media Connection –Public Communication and its Repercussions. Sociology of the Sciences Yearbook, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2085-5_3
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